tag: machine learning

Conflicting Demands At The Edge
By Ed Sperling - 04 Jun, 2020 - Comments: 0

Semiconductor Engineering sat down to define what the edge will look like with Jeff DeAngelis, managing director of the Industrial and Healthcare Business Unit at Maxim Integrated; Norman Chang, chief technologist at Ansys; Andrew Grant, senior director of artificial intelligence at Imagination Technologies; Thomas Ensergueix, senior director of the automotive and IoT line of business at Arm; V... » read more

(Artificially) Intelligent Verification
By Brian Bailey - 01 Jun, 2020 - Comments: 1

Functional verification produces a lot of data, , but does that make it suitable for Artificial Intelligence (AI) or Machine Learning (ML)? Experts weigh in about where and how AI can help and what the industry could do to improve the benefits. "It's not necessarily the quantity," says Harry Foster, chief scientist for verification at Mentor, a Siemens Business. "It's the quality that matter... » read more

A Different View On Debugging
By Hagai Arbel - 01 Jun, 2020 - Comments: 0

The classic approach to improve an engineering task that is becoming too complex due to its size and detail is to raise the abstraction of design representation. In this way we plan cities, build aircraft and plan 500M gate SoCs. For example, there is no way an ASIC design could go beyond a few thousand logic gates without shifting abstraction to the Register Transfer Level (RTL) and leveragin... » read more

FPGA Prototyping Complexity Rising
By Ann Mutschler - 28 May, 2020 - Comments: 0

Multi-FPGA prototyping of ASIC and SoC designs allows verification teams to achieve the highest clock rates among emulation techniques, but setting up the design for prototyping is complicated and challenging. This is where machine learning and other new approaches are beginning to help. The underlying problem is that designs are becoming so large and complex that they have to be partitioned... » read more

What Will The Next-Gen Verification Flow Look Like?
By Ed Sperling - 28 May, 2020 - Comments: 0

Semiconductor Engineering sat down to discuss what's ahead for verification with Daniel Schostak, Arm fellow and verification architect; Ty Garibay, vice president of hardware engineering at Mythic; Balachandran Rajendran, CTO at Dell EMC; Saad Godil, director of applied deep learning research at Nvidia; and Nasr Ullah, senior director of performance architecture at SiFive. What follows are exc... » read more

Powering The Edge
By Arm - 13 May, 2020 - Comments: 0

On-device machine learning (ML) is a phenomenon that has exploded in popularity. Smart devices that are able to make independent decisions, acting on locally generated data, are hailed as the future of compute for consumer devices: on-device processing slashes latency; increases reliability and safety; boosts privacy and security...all while saving on power and cost. Although ML in edge d... » read more

Sensors, Data And Machine Learning
By Ed Sperling - 12 May, 2020 - Comments: 0

Strategies for building reliability into chips and systems are beginning to shift as more sensors are added into these devices and machine learning is applied to that data. In the past, system monitoring relied heavily on MEMS devices for things like acceleration, temperature and positioning (gyroscopes). While those devices are still important, in the past couple years there has been an exp... » read more

New Uses For Manufacturing Data
By Ed Sperling - 12 May, 2020 - Comments: 0

The semiconductor industry is becoming more reliant on data analytics to ensure that a chip will work as expected over its projected lifetime, but that data is frequently inconsistent or incomplete, and some of the most useful data is being hoarded by companies for competitive reasons. The volume of data is rising at each new process node, where there are simply more things to keep track of,... » read more

Manufacturing Bits: May 11
By Mark LaPedus - 11 May, 2020 - Comments: 0

Covid-19 data mining Using machine learning and other technologies, Lawrence Berkeley National Laboratory (Berkeley Lab) has developed a data text-mining tool to help synthesize a growing amount of scientific literature on Covid-19. Each day, some 200 new journal articles are being published on the coronavirus alone, according to Berkeley Lab. Berkeley Lab’s data mining tool, which is liv... » read more

Inference Moves To The Network
By Bryon Moyer - 07 May, 2020 - Comments: 0

Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future inference capabilities will reside not only at the extremes of the data center and a data-gathering device, but at multiple points in between. “Inference isn't a function that has to resid... » read more

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